import contextlib
import gc
import tempfile
from collections import OrderedDict
from typing import Dict, List, TypedDict
from unittest.mock import MagicMock, patch

import pytest
import ray
import torch
import torch.nn as nn
from huggingface_hub import snapshot_download

import vllm
from vllm.config import LoRAConfig
from vllm.distributed import (destroy_distributed_environment,
                              destroy_model_parallel,
                              init_distributed_environment,
                              initialize_model_parallel)
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                               MergedColumnParallelLinear,
                                               RowParallelLinear)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.sampler import Sampler
from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
from vllm.model_executor.model_loader import get_model


class ContextIDInfo(TypedDict):
    lora_id: int
    context_length: str


class ContextInfo(TypedDict):
    lora: str
    context_length: str


LONG_LORA_INFOS: List[ContextIDInfo] = [{
    "lora_id": 1,
    "context_length": "16k",
}, {
    "lora_id": 2,
    "context_length": "16k",
}, {
    "lora_id": 3,
    "context_length": "32k",
}]


def cleanup():
    destroy_model_parallel()
    destroy_distributed_environment()
    with contextlib.suppress(AssertionError):
        torch.distributed.destroy_process_group()
    gc.collect()
    torch.cuda.empty_cache()
    ray.shutdown()


@pytest.fixture()
def should_do_global_cleanup_after_test(request) -> bool:
    """Allow subdirectories to skip global cleanup by overriding this fixture.
    This can provide a ~10x speedup for non-GPU unit tests since they don't need
    to initialize torch.
    """

    if request.node.get_closest_marker("skip_global_cleanup"):
        return False

    return True


@pytest.fixture(autouse=True)
def cleanup_fixture(should_do_global_cleanup_after_test: bool):
    yield
    if should_do_global_cleanup_after_test:
        cleanup()


@pytest.fixture
def dist_init():
    temp_file = tempfile.mkstemp()[1]
    init_distributed_environment(
        world_size=1,
        rank=0,
        distributed_init_method=f"file://{temp_file}",
        local_rank=0,
        backend="nccl",
    )
    initialize_model_parallel(1, 1)
    yield
    cleanup()


@pytest.fixture
def dist_init_torch_only():
    if torch.distributed.is_initialized():
        return
    temp_file = tempfile.mkstemp()[1]
    torch.distributed.init_process_group(
        backend="nccl",
        world_size=1,
        rank=0,
        init_method=f"file://{temp_file}",
    )


@pytest.fixture
def dummy_model() -> nn.Module:
    model = nn.Sequential(
        OrderedDict([
            ("dense1", ColumnParallelLinear(764, 100)),
            ("dense2", RowParallelLinear(100, 50)),
            (
                "layer1",
                nn.Sequential(
                    OrderedDict([
                        ("dense1", ColumnParallelLinear(100, 10)),
                        ("dense2", RowParallelLinear(10, 50)),
                    ])),
            ),
            ("act2", nn.ReLU()),
            ("output", ColumnParallelLinear(50, 10)),
            ("outact", nn.Sigmoid()),
            # Special handling for lm_head & sampler
            ("lm_head", ParallelLMHead(512, 10)),
            ("logits_processor", LogitsProcessor(512)),
            ("sampler", Sampler())
        ]))
    model.config = MagicMock()
    return model


@pytest.fixture
def dummy_model_gate_up() -> nn.Module:
    model = nn.Sequential(
        OrderedDict([
            ("dense1", ColumnParallelLinear(764, 100)),
            ("dense2", RowParallelLinear(100, 50)),
            (
                "layer1",
                nn.Sequential(
                    OrderedDict([
                        ("dense1", ColumnParallelLinear(100, 10)),
                        ("dense2", RowParallelLinear(10, 50)),
                    ])),
            ),
            ("act2", nn.ReLU()),
            ("gate_up_proj", MergedColumnParallelLinear(50, [5, 5])),
            ("outact", nn.Sigmoid()),
            # Special handling for lm_head & sampler
            ("lm_head", ParallelLMHead(512, 10)),
            ("logits_processor", LogitsProcessor(512)),
            ("sampler", Sampler())
        ]))
    model.config = MagicMock()
    return model


@pytest.fixture(scope="session")
def sql_lora_files():
    return snapshot_download(repo_id="yard1/llama-2-7b-sql-lora-test")


@pytest.fixture(scope="session")
def mixtral_lora_files():
    return snapshot_download(repo_id="terrysun/mixtral-lora-adapter")


@pytest.fixture(scope="session")
def gemma_lora_files():
    return snapshot_download(repo_id="wskwon/gemma-7b-test-lora")


@pytest.fixture(scope="session")
def chatglm3_lora_files():
    return snapshot_download(repo_id="jeeejeee/chatglm3-text2sql-spider")


@pytest.fixture(scope="session")
def baichuan_lora_files():
    return snapshot_download(repo_id="jeeejeee/baichuan7b-text2sql-spider")


@pytest.fixture(scope="session")
def baichuan_zero_lora_files():
    # all the lora_B weights are initialized to zero.
    return snapshot_download(repo_id="jeeejeee/baichuan7b-zero-init")


@pytest.fixture(scope="session")
def tinyllama_lora_files():
    return snapshot_download(repo_id="jashing/tinyllama-colorist-lora")


@pytest.fixture(scope="session")
def phi2_lora_files():
    return snapshot_download(repo_id="isotr0py/phi-2-test-sql-lora")


@pytest.fixture(scope="session")
def long_context_lora_files_16k_1():
    return snapshot_download(repo_id="SangBinCho/long_context_16k_testing_1")


@pytest.fixture(scope="session")
def long_context_lora_files_16k_2():
    return snapshot_download(repo_id="SangBinCho/long_context_16k_testing_2")


@pytest.fixture(scope="session")
def long_context_lora_files_32k():
    return snapshot_download(repo_id="SangBinCho/long_context_32k_testing")


@pytest.fixture(scope="session")
def long_context_infos(long_context_lora_files_16k_1,
                       long_context_lora_files_16k_2,
                       long_context_lora_files_32k):
    cleanup()
    infos: Dict[int, ContextInfo] = {}
    for lora_checkpoint_info in LONG_LORA_INFOS:
        lora_id = lora_checkpoint_info["lora_id"]
        if lora_id == 1:
            lora = long_context_lora_files_16k_1
        elif lora_id == 2:
            lora = long_context_lora_files_16k_2
        elif lora_id == 3:
            lora = long_context_lora_files_32k
        else:
            raise AssertionError("Unknown lora id")
        infos[lora_id] = {
            "context_length": lora_checkpoint_info["context_length"],
            "lora": lora,
        }
    return infos


@pytest.fixture
def llama_2_7b_engine_extra_embeddings():
    cleanup()
    get_model_old = get_model

    def get_model_patched(*, model_config, device_config, **kwargs):
        kwargs["lora_config"] = LoRAConfig(max_loras=4, max_lora_rank=8)
        return get_model_old(model_config=model_config,
                             device_config=device_config,
                             **kwargs)

    with patch("vllm.worker.model_runner.get_model", get_model_patched):
        engine = vllm.LLM("meta-llama/Llama-2-7b-hf", enable_lora=False)
    yield engine.llm_engine
    del engine
    cleanup()


@pytest.fixture
def llama_2_7b_model_extra_embeddings(llama_2_7b_engine_extra_embeddings):
    yield (llama_2_7b_engine_extra_embeddings.model_executor.driver_worker.
           model_runner.model)
